We followed the evaluation protocol introduced by [1]} and adopted by most subsequent works; we considered a testing subset fixed for all the experiments (100 images for VGG Cells and ADI Cells, and 10 images for MBM Cells) and training and validation subsets of varying size (\(N\)  images for each subset) to simulate lower or higher numbers of labeled examples.
Following previous work, we set \(N\)  to 16, 32, and 50 for VGG Cells, to 10, 25, and 50 for ADI Cells, and to 5, 10, 15 for MBM Cells. Concerning BCData, we instead used the training, validation and testing splits provided by [2]}.
As performance metrics, we adopted the widely used mae already defined in sec:back:visual-counting:metrics.
For VGG Cells, MBM Cells, and ADI Cells, we repeated the experiment 10 times, randomly sampling different splits for each configuration, and we reported the mean and standard deviation of the evaluation metric. To check the consistency of the results on these random splits, we also re-implemented the original FCRN-A method presented in [3]}, thus performing an exact head-to-head comparison with the same samples being used for training and testing.
Concerning BCData, we reported the mean and the standard deviation of the MAE calculated between 10 runs over the 402 images comprising the test split, changing the random initialization seed each time.
